# -- coding: utf-8 --
import mnistData
import tensorflow as tf
mnist = mnistData.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
# 计算交叉熵
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
#TensorFlow用梯度下降算法（gradient descent algorithm）以0.01的学习速率最小化交叉熵
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(10000):
  print "this is " + str(i + 1) + " train"
  #每个循环随机抓取100个批处理点
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict = {x: batch_xs, y_: batch_ys})

# begin estimate
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print sess.run(accuracy, feed_dict = {x: mnist.test.images, y_: mnist.test.labels})
print 'end'






